{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "# from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.utils import shuffle\n",
    "from __future__ import print_function\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 2) (150, 1)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "# cifar10_dir = 'cs231n/datasets/cifar-10-python/cifar-10-batches-py'\n",
    "# X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# # As a sanity check, we print out the size of the training and test data.\n",
    "# print('Training data shape: ', X_train.shape)\n",
    "# print('Training labels shape: ', y_train.shape)\n",
    "# print('Test data shape: ', X_test.shape)\n",
    "# print('Test labels shape: ', y_test.shape)\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]\n",
    "y = iris.target.reshape((-1, 1))\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train= (105, 4)\n",
      "X_test= (45, 4)\n",
      "y_train= (105, 1)\n",
      "y_test= (45, 1)\n"
     ]
    }
   ],
   "source": [
    "X, y = shuffle(iris.data, iris.target, random_state=13)\n",
    "X = X.astype(np.float32)\n",
    "\n",
    "# 训练集与测试集的简单划分\n",
    "offset = int(X.shape[0] * 0.7)\n",
    "X_train, y_train = X[:offset], y[:offset]\n",
    "X_test, y_test = X[offset:], y[offset:]\n",
    "y_train = y_train.reshape((-1,1))\n",
    "y_test = y_test.reshape((-1,1))\n",
    "\n",
    "print('X_train=', X_train.shape)\n",
    "print('X_test=', X_test.shape)\n",
    "print('y_train=', y_train.shape)\n",
    "print('y_test=', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from k_nearest_neighbor import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45, 105)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2pmSspZImZLYmA7HEY5oSFUrzMSUQnHDCCUH7z3/+g/FkIKf5dOWVV8Z4ei7q+5QoIbGBnzRKHqI5TuLPI2q/0i8yNPfTPFNKaKIyKTRHlwzoNP7o/ZOpW5IGDRoUNCpHQ+OZkqkknjuPOOKIoF1xxRUYT58dNM4aNmyI8X379g3au+++W5EBvZqf+ZpLuqdm4l9Q0m3/10LKGGOMMea3yE9eTOWcx0qK+bLGGGOMMf9FeGsEY4wxxpgq8GLKGGOMMaYKqtoBva60atUqH3roobNpJRPnk08+GTQy/JHZVGJjMe3kSteReBfyP/3pT0Fr2bJl0EpZi59++mnQaBdcek5J6tmzZ9Box9327dtjPO2AftJJJwXt/PPPx/jHH388aKNHjw4a7XQusTF9s802q+ieaFdqic36HTt2DNo333yD8WeccUZFx1522WUY/8UXXwSNdpEu7WBOO6A3btw4aNSfJTbw0w7iI0aMwHjq0++++27QaFdx2oFbktZdNyT16v777w/aX/7yF4wnY/jGG28ctNLYp+uTWZ6eXWJj7T777BO0jTbaCOPpvuj+l1566aD99a9/xXPSPEnvv2RAp7mDdnumtpf4/ZVMxAQlv9AcS0lCkrTCCisEjXYL79ChA8bTPElJFd9++23QSjug0/OTqZ4SOiTpggsuCBr1s65du2L8HXfcETQyUNPu8xJXFKFEg1JFiy222CJoe++9d9D22GOPoNHnniQ98ki0XdPu9aXPGKqUQEk2lGQkSbvsskvQLrzwQu+AbowxxhjzS+PFlDHGGGNMFXgxZYwxxhhTBV5MGWOMMcZUgRdTxhhjjDFVMFez+Ro2bJiXX3752bTFF18cj915552DRiUdSlv9184alDgD4I033sD4Xr16BW3gwIFBo636S+VkFllkkaBR9kMpo4WOpSwzyhKSpIkTJwaNSo+cc845GE/b+lOmyz333IPxlEFB8fScr7zyCp7z0UcfDVrtkkWla0vSkUceGbRp06YF7frrr8d4Gj9U5qJUToYyx2bOnBk0evcSl0q4+eabgzZjxgyMpz5J2UedOnUK2pZbbonnpMxByvyhMSZx+Ym77747aNROErc/lacaPHgwxtPcQ5lTkyZNwnjK5tt9992DRuVA+vfvj+f85JNPgrbGGnHPZCrZJElHHXVU0B566KGgUdmX0rGUdVzKmqWs05NPPjlolKEn8Ty9wALxuwAq5SNxJjW1H2Vi03iUeJ668sorg0YlViQu8XPbbbcFrZRNSPPMiSeeGDR6dok/O6hE0fbbb4/xlOVH2c00d/Xp0wfPSZnMlKFHZbgkzo6mcUbznsSZxK+//rqz+Ywxxhhjfmm8mDLGGGOMqQIvpowxxhhjqsCLKWOMMcaYKpjrBvTllltuNo2MlZK01VZbBW3y5MlBa926NcY3aNCgomNL5WRatWoVtOHDhwdtmWWWCVr9+vXxnPSsZPgrtQmdl95JqZwMGYPJxDpy5EiMX2+99YJGBuhSm1ACwB/+8IegUVIBlTORpH79+gWNzNJUJkJiw+PUqVODRv1JYnMnmYBLbfLBBx8ErV69ekGjZ5LYWE4m4JKBn8onjB07Nmj0/t9++2085/PPPx80Mqu/9tprGE8GbuqTpXIydK809ih5QWKzPJUz2nDDDTGeTNDUT6idSmbjCRMmBI3GTqkcTMOGDYNG7UcJNRL3X0oeKiUF0LGUEEPlqSR+V1TipjROqHQRlZ6h8VQyoFOiA8WXEhWoFNkzzzwTNEqekLg810477RS0zz//HOPHjRsXNJq7qJ9JbECn0jdklC8laT3xxBNBo7mj9BlLn0d0rYUWWgjj6f2PGzfOBnRjjDHGmF8aL6aMMcYYY6rAiyljjDHGmCqY42IqpXRDSumTlNIbP9KapZSGppTG1Pybd940xhhjjPmNw27H2blJ0pWSfryt8kmSnsg5X5RSOqnmz3Hr1VrUq1dPTZs2nU0rGXO32WaboJ1xxhlBe/311zGedqK9/PLLg0bGTkk6+OCDg0aGSTLr0s68EhsOyWh//vnnY/xFF10UNNrxtmQMbt68edDIgPzyyy9j/EYbbRQ0Msz27t0b42nXWdpZ+uqrrw4a7UwrsbmRjO4pJYynnXTJxHj88cdj/KKLLho0MqbusMMOGH/ttdcGjcyRu+66K8b/5z//CdqFF14YtNqJHz8wdOjQoH388cdBe//994NGfU/i3a5pPD/33HMYT2OCTLSl3b7pXsmAXDLB0g7oZIB+8cUXMZ7M8jT3fPXVV0E77bTT8JxkIH/88ceDVnonNB+SKb9LF/bZ3n///UGj+aguO6DTfL7tttti/HvvvRc06j+l51977bWD9uqrrwZt4YUXDhqNcYl326ad5ku7stPcS5UaHnzwQYxv165d0GieKCUVnHXWWUGj5I3S3EXGfDLF09y9//774znXWWedoJGpvnv37hhP/Zx2QKd2lng9QEZ9Yo7fTOWcn5ZUe4/4HSX9UPegv6T4tMYYY4wx/wX8VM9U85zzREmq+TcXgzPGGGOM+Y1Tyc98VZFS6i6pu1TeG8IYY4wxZn7lp34zNTml1EKSav7NO29Kyjn3yzl3yTl3KW1IZ4wxxhgzv/JTF1NDJO1X89/7Sbrv57kdY4wxxpj5izmWk0kpDZS0saQlJU2WdKakeyXdIWlZSR9J+lPOubZJPdCyZcv85z//eTbto48+wmMpg4Gy3KjsiySdcMIJQRs8eHDQnnrqKYw/88wzg3bAAQcEjTLkKGtN4owkyiY777zzMP7II48M2gUXXBA0yvKQuJwMZd4dffTRGE8ZPVS+gkoCSNLdd98dtE033TRovXr1Cto//vEPPCdlw1GZiFKWEWWkUZZX3759MZ5Kz1CJmGuuuQbje/ToEbQmTZoErZThSZl/f/3rX4P20ksvYTxl/1CbUtmhjTfeGM+5ySabBI0yLEv9/LrrrgsalXgplZOhjCDKSNp9990x/tZbbw3avvvuGzTKbpU4a5eyGalE0d///nc8J/WpzTbbLGhjxozBeMo8o3FO15G4rSnzkEohSZxRRVmnjzzyCMYvv/zyQaN3usoqq2A8ZXNSJjmN/ZI9heaOv/3tb0EbP348xl966aVBo9IxpX565513Bm3AgAFBK2Wt0mcHlXiiz01J2n777YO2xx57BI0+t+jZJc5QpTn+vvv4+5s999wzaM2aNQsa9UdJ2meffYLWu3fvisrJzPF3t5xzvLtZxE9BY4wxxpj/MrwDujHGGGNMFXgxZYwxxhhTBV5MGWOMMcZUwVzdq2DatGnBCEtmTUm64YYbgkaGRSodUYrfcsstg7bYYothPBlB6ZzDhg0LGpWJkNisTka8kll54MCBQaOSDKXyAWTWv/fee4N2+umnYzwZTskATaZySdpwww2DRibKK664ouJz0nYb9Pzff/89xtO7phItdSkns/jisVQlGVMl6frrrw8ameWpTIPEpW9OOumkoK288soYv/nmmweN+gmV4ymV+HnhhReCdswxxwTtiCOOwHjqZ7fffnvQSqZ6Kt0yaNCgoFEpI4kTJcjYWyr9QgkoZJglEyy9J4lNtGQgprIzEj8rzSctWrTA+HPOOSdoVPaJxo7EJZYOPfTQoJXM1m+++WbQ9ttvv6A1atQI43/3u98FjcrRUPJH6Zn69+8ftGOPPTZo1HYSj/2bb745aNtttx3G//GPfwzao48+GjQqGySxgZ7KCdFzStI999wTNGpTeiZK6JC4nSgpYe+998b42267LWg0zkpJYtQmleJvpowxxhhjqsCLKWOMMcaYKvBiyhhjjDGmCryYMsYYY4ypgrlqQG/UqFHYSfnDDz/EY2kn2IsuuihoyyyzDMb37NkzaGS2fvrppzGeDG61d2+X2DC38MIL4zlpB3TaHZaM9hLvLnv22WcHrWQ2ph3QacdY2uldkk455ZSg0Y7LpV3lyYRLO4BT25FZVJL69esXtE6dOgVt2rRpGH/ggQcGjUzdJQM5JRuMHTu2outI/Kxkat9tt90wnsyx9E6o7SVpyJAhQXvvvfeCRjugU9+XeLdxSug46qijMJ6MsXTO0vXXXnvtoO20005B+9Of/oTxpNPYKyVqtG7duqJzzpgxI2il5BPamZySMmj3eonfHyVVlHZAp2NpjqREBYmflUztpUoHZIynnbFLO6BTBQPa7Zp2Cy/VlKU+Qab8UpWPbt26VaSVkk8oUWLnnXcOWikh6dxzzw0aGf3pnBInC1DyxyGHHBK0iy++GM9J1TNo7JKpXeJ+vsQSSwSN5niJkxoqxd9MGWOMMcZUgRdTxhhjjDFV4MWUMcYYY0wVeDFljDHGGFMFXkwZY4wxxlRByjnPtYsttNBCmZz1BGW/UOYdlSmQpDXXXDNolHlVyjzbYostgkbb51P5BiqxIklTp04N2tZbbx20UaNGYTxlKQ0dOjRoK664Isa//PLLQdt///2DVtpSn7JfKEvniy++wPhnn302aJ07dw4aZSOWymRcd911QevYsWPQqESLJK233npBo34yevRojKdnpYyoUubYY489FrQFFoh/xyll802fPj1or7zyStDeeustjKdyMpSNSOOpQYMGeE4qJ7PVVlsF7aabbsJ4GnuUCVoq00GZh1QiZ/DgwRhP2UtUzmbZZZfF+BVWWKGi61OGaamdxo0bF7SNNtooaKVsvnXXXTdolDlHfU/i8bPxxhsHrZQ1u+SSSwbtoYceClopE5qe63/+53+C1qFDB4ynjLimTZvisbUpfUb+/ve/D9rIkSODNmnSJIzfdtttg0blUGg8SDz3U9blp59+ivEPPvhg0Gg+33777TGexiRlUtN4HD58OJ6TPvsoa5SyDiWpXbt2QaPMvVI/o7JRn3/++cicc6yzUwt/M2WMMcYYUwVeTBljjDHGVIEXU8YYY4wxVTDHxVRK6YaU0icppTd+pJ2VUhqfUnq15p9tftnbNMYYY4yZN6mknMxNkq6UVHv/9j455951udiSSy4ZSpV8/PHHeCyVfjn11FOD1rJlS4ynMiu33HJL0FZbbTWMv+yyy4JGJW6onE2jRo3wnJ988klF90RlWyTpsMMOCxpty9++fXuMf+mll4LWu3dswl69emH8448/HjQyZpOxVJLatGkTNCqJQeVEyKgtsZGQTJBUdkXikgpTpkwJGpVJKB1LBnQqpyJxqQXqP1R6Q2ITLbUpGWMladdddw0alZOhRIFNNtkEz0nGXCrHc/LJJ2M8lYqg5AfqT5LUpUv0ilKiR9euXTH+hhtuCBqN/Q033BDjyZhOBvwmTZoE7a9//Suek0qS0Pun8k4Sl62i8lglA/tmm20WNBo7ZGCWpC+//DJoVB7s4YcfxnhKFCEDd6mcDJWJoXmCEjrq1auH5zzzzDODRmVrSp9x5513XtColBeVrZH4s4/6z9dff43x9P7/9a9/BW3gwIEYT2OKxsnBBx8ctCuvvBLPSfM8zR2lEjtUDoaS3kqfBzTPlOb+2szxm6mc89OSOD3LGGOMMea/nGo8U4enlF6v+Rlw8Z/tjowxxhhj5iN+6mLqakkrSuosaaKk+JtYDSml7imlESmlEaVKzcYYY4wx8ys/aTGVc56cc56Rc54p6e+S4o5w///YfjnnLjnnLiUvkTHGGGPM/EpFO6CnlJaX9EDOedWaP7fIOU+s+e9ekv4n58xuzh/RoEGDXNs0VzKQ77nnnkE74YQTgkY7nkrSUUcdFTQyNz7zzDMYT2Z3MheSua+0MzQtJmkH8ksuuQTjzzjjjKCRiZB2G5Z4J9iTTjopaOeffz7Gn3baaUEjY+f999+P8bS7bfPmzYNGRvtSO9EuwAsuGPMqSiZS6lPUpv3798f4CRMmBI3e/7777ovxV199ddBSSkGjdpL4/ffp0ydo9evXx3gyS5OBnnaVp+QBSXr00UeDtssuuwSN+q4k9ejRI2hkCqeKAhI/E80nlPwh8Y7L1P6TJ0/GeJrTyBhLJlgy20rcfiuttFLQSgb0Y445Jmg0z1D1AYn7xBFHHBG0krGXEiWoTUsVMsjETZ9dq666Ksa/9tprQaP2o/tfZJFF8JyUKEPG6tJO/WTWpqoAiy/OLhra2Zzm6M8//xzjKXnd+QmaAAAgAElEQVSJ5q7u3btjPO3W/u9//ztolSZOSZxQRP2slNBz0EEHBY3atFRRgz7jR48eXdEO6HPM5kspDZS0saQlU0rjJJ0paeOUUmdJWdIHkmJKkjHGGGPMfwFzXEzlnONf6SQu3maMMcYY81+Gd0A3xhhjjKkCL6aMMcYYY6rAiyljjDHGmCqopJzMz0b9+vW1/PLLz6ZROZaSvtxyywWNSrRInEFBJRkoy6R0fcp0ef/994NWyhxr3bp10CibjcpMSJyltOiiiwZt4sSJGN+2bdug1W4Pqdwmiy22WNAWWmihoFGWicT3T1k21HalZ1pqqaWCRpk/lCEncfYKvdMvvuAiAHR90lq1aoXx1Cco04TencSZKo0bNw4a9TNJGjduXNAom/Gtt94KWilDkcoZ0f2X+jm1Cb0/ymSUuCQKzR2lTGAqU0OZc6UsKSqxRM//1VdfBY3KmUic5TRp0qSgUTaVJLVo0SJoHTp0CFopm4/mOepT3377LcZTlh6Vnim1Cc2p77zzTtBKpVvoWSm7mZ6J5j2Jsza/+eaboJWyu6lPrLDCCkGjEi8Svyvq5zSfSTzP02dEKeOe+sp9990XtBVXXDFo06ZNw3OuvvrqQaPxOHbsWIyne6VxVtqmid4/jWfC30wZY4wxxlSBF1PGGGOMMVXgxZQxxhhjTBV4MWWMMcYYUwUVlZP5uWjRokWuvd37hx9+iMc++OCDQbvwwguDVjJLH3fccUG75557gvbUU09h/FlnnRU02qqeDItkFpWkzz77LGjXXHNN0ErlXGhbfTq2ZOJ86aWXKrp+qXwAvT8y5/3jH//A+CFDhgRt4403Dtrhhx8etOeeew7P2a9fv6B16tQpaKUi21QSggzY9J4k6csvvwwamXVLZUKoTal8xQUXXIDxZOKlUgvU9pK08847B+29994LWufOnYO23nrr4Tm33377oFE5oGOPPRbjqcTOjjvuGDQypkrS2muvHbRtttkmaN26dcP422+/PWhUjuYPf/gDxtN97bTTTkGjpIhrr70Wz0nlXKicT6mczIEHHhg0KqVUMvb+/ve/D1qpxBFB44/K2QwbNgzjKQGB2nSVVVbB+Msvv7yiYympYYEF+DsHmjtonih9xtHzU9mhP/3pTxh/9913B43maPrckbiky5tvvhm0UimtPfbYoyKNShnRZ7kkPf7440Gjsk8DBw7E+F133TVolBBU+jygpJo+ffpUVE7G30wZY4wxxlSBF1PGGGOMMVXgxZQxxhhjTBV4MWWMMcYYUwVz1YBev379XNsMVtoFmYyxd9xxR9BKO5iTCZbM0v/85z8xngynd955Z9CmTJkSNNqBWuJdV8lE+sQTT2A8mevIsEq7uEr8/GQiJWOiJB166KFBo52JX3/9dYx/8cUXg0b3uuWWWwattAP5rbfeGjTaBbi0MzQZJmkX40ceeQTjacdn6hN77bUXxpO5kpIqqD9KbKSk5I1SpYBVV101aLSz9BprrBG01VZbDc85YsSIoFE/79u3L8aT4XbQoEFBK5lI11xzzaBRosP999+P8dtuu23QBg8eHLSZM2di/NJLLx20TTbZJGiU6PDwww/jOcnEvNlmmwWN2k7id0rPX0roGTVqVNCoT9LYkXhX+xtvvDFopbmLdrUnU3op/pVXXgkaVWqgig6lHdC32mqroJEpvPROyGxOyQ9UpULitj7++OODVtqpn+ZOqr6wyy67YDzNiWTW33333YM2YMAAPCdVdKBEETLaSzx267IDOt3XBx98YAO6McYYY8wvjRdTxhhjjDFV4MWUMcYYY0wVeDFljDHGGFMFc1xMpZTapJSGpZRGpZTeTCkdVaM3SykNTSmNqfn34r/87RpjjDHGzFssWMEx30s6Nuf8ckqpsaSRKaWhkvaX9ETO+aKU0kmSTpIU99f/ETNnzgwZOKVMB8qIojIttFW8xGU+KMuGSneUrk8ZZZRNWMoUoGel6yy66KIYTxlZdP3SO6GMoEmTJgVtwQW5W0ybNi1oVH6BMnckztSgcjyU5dSsWTM8Z4MGDSo6lrJUJO4n9JzUdyTOlKE2LWXUfP/990Gjd1rKxqNsQurTpZIS9Fz0Tlq0aFHxOceNGxc0uv/SOJk6dWrQ6P21bNkS4+mZJk+eHLQJEyZgPN0rjZOVVlqp4uvTOamflfo5zR00zktZr6RTO1N5otJ90TlL8zllPi6+ePz7N/Udidu/Q4cOQSuNU2p/ytKj7PZSxjv1f5r7qD9L3CfoOddZZx2Mf/bZZ4NGGYqkSTx3Tpw4saJ7knj8rLXWWhWdk9pe4lJcdP+lrFO6VxpnpX5aeleVMMdvpnLOE3POL9f899eSRklqJWlHST8U7ekvKRafMsYYY4z5jVMnz1RKaXlJa0oaLql5znmiNGvBJYn/SmCMMcYY8xumkp/5JEkppUUl3S3p6JzzV1TxvBDXXVL3mv/+KfdojDHGGDPPUtE3UymlhTRrITUg5/zDVsCTU0otav5/C0lo6sg598s5d8k5d6HdUY0xxhhj5mfm+M1UmvV10vWSRuWc//Kj/zVE0n6SLqr5931zOtcSSyyhrl27zqa1bt0aj6XSKWSYfO+99zD+yCOPDNr6669fkSZJxx13XNDIsEgmPNq+XmKzdc+ePYN2ySWXYPyJJ0Z/P5kbP/jgA4yn8gennHJK0MiwJ0mHHXZY0OrXrx+09dZbD+MPPvjgoD3zzDNB+8tf/hK0UukSKjtEJkYqEyFJvXr1ChqVKaFnl6Rll102aP/zP/9TcTwZe6n0zQknnIDxZOo/4ogjgnb00UdjPJW/aNeuXdDI7HrffTzkaUxReaeS2ZjGbqWmcolLhwwZMiRoVHZIUpijJC5d8vTTT2M8mdX79OkTNEq0oJJNEvepe++9N2irrLIKxlOZETJLl+ZjKuV17LHHBo36o8RmeXrWUumSt956K2iPPvpo0B5//HGMP/nkk4P25ptvBo1KgZWeiebjww8/PGg0niQe09TPqcSMxHMXPWcpeYUM9Pvuu2/QqByMJLVt2zZod911V9BuuOGGoO20E1usjzrqqKAdcsghQdthhx0w/qqrrgoafR6XEs8ooevCCy/EY2tTyc98G0jaR9K/Ukqv1minaNYi6o6U0kGSPpIUiz8ZY4wxxvzGmeNiKuf8rKSS2SlWLzXGGGOM+S/CJiZjjDHGmCrwYsoYY4wxpgoq3hrh52Dq1KnBNNmkSRM8lky4I0eODBoZoCXp1ltvDRqZOJ9//nmMv/nmm4NGhlvaBbi0gzjdKxnh3njjDYy/5ZZbgjZs2LCgLb/88hhPZmMydpOJUOJdZ+vVqxe0NdZYA+PvueeeoJEJltquW7dueM5BgwYFjYzFZCqXuJ3pWNrFV+Jd5Z966qmglXa1pzahnYmp7SW+1/HjxweNzLYS95V//etfQaM2pXcn8TilcfLQQw9hPO1sfueddwaNdn+XpJVXXjlolY5nSZo+fXrQaEyU+lSbNm0quj4ZY0um+hdeeCFoG2+8cdBKCTk33XRT0B577LGglXZgJ1M/PVNdDOg091JFAEkaNWpU0JZYYomglYzNZNan3bKpUkJpPu/fv3/Q3nnnnaCVPmNo7JHZvLTb98CBAys6Z8mATn2KkndKu4XT+KUd9AcMGBA0Sr6QeJxT5RMao1Ll46xkQKfrV4q/mTLGGGOMqQIvpowxxhhjqsCLKWOMMcaYKvBiyhhjjDGmCryYMsYYY4ypgrmazdewYUN17NhxNq1FixZ47DrrrBO0Bx98MGiUPVGKv/7664NGGSGleMpyomyuUqYBlaSgDMMnn3wS49ddd92gUfbBmDFjMJ4y9+g5V111VYzv0qVL0ChDkd5z6fqUjXjOOecE7f7778dztm/fPmiUkUJZhxK/U8rSKj0TlX9YYYUVKrqOJL322mtBmzlzZtCon0h8r1Q6Zc0118R4yuih0juUoUelkCTO6KHnp6y70rGUpVYqRzNixIignXHGGUGjsSvxu3733XeDVion8/bbbwfttNNOCxplGVHmk8T9nDLcShmOVOKJSrRQ35U4w5jmjtL1KWuX5v5SP6WMrhdffDFopSyxtddeO2g09ijrtpTNR/30xhtvDFppPqX3R33nkUcewfhNN417ZtM9ffrppxhP2aw0R5fmHspwpgxHes5SiZZKn+mss87CeDq2Ltl81Cdo7BP+ZsoYY4wxpgq8mDLGGGOMqQIvpowxxhhjqsCLKWOMMcaYKpirBvSFF15Yq6+++mxayYDeoUOHoHXu3BnPSay00kpBIyMhnVOSVllllaBRSY2mTZsGrVRSoXXr1hVdZ6211sL4Tp06BY0Mm3RPEhshSavdRj+w4oorBo0Mm6USQXTezz77LGhkTKbyQpK0/vrrB22xxRYLWs4Z4+lalEBQKpFDpR4oqYHaTuJ3QvdaO3HjB+i9kFm5ZPYePXp00GjsUPIFjVGJTfH0/GR2laR27dpVdOziiy+O8VSShO611M/pXVP7k7FV4j5Nz//ll19WdJzEfYqM9qV2pnmG5o5SKSoyoNM7Lc19zZs3Dxr1s9I4o7l7ypQpQSMDt1Se52tD89kCC/B3DtRP6J1SkorEcy/189Iz0buic06aNAnj6Vp0ztI4Hzt2bNDI6F/pdUo6jYmSqZ/uderUqUErGdCpn1AZNMLfTBljjDHGVIEXU8YYY4wxVeDFlDHGGGNMFcxxMZVSapNSGpZSGpVSejOldFSNflZKaXxK6dWaf7b55W/XGGOMMWbeohID+veSjs05v5xSaixpZEppaM3/65Nz7l3pxVJKYSdqMlFK0iWXXBK0008/PWjbb789xu+0005BGzx4cND+8Y9/YPxGG20UtD333DNoZI4rmeI/+eSTiu6TdmuW2LBJ72mvvfbCeNoxmAyrhx12GMYfdNBBQaMd6B9++GGMv/fee4N23nnnBY3eCe2ULknXXHNN0LbZJq7ryRQtSVtssUXQaBflK664AuPJ3Eg7sP/hD3/A+O7duweNTLBt2rTBeDr28ssvDxrtYC5J1157bdBot3Halb5k4P7jH/8YtB49egTtuOOOw/iuXbsGjdqpZMrfd999g0bvb//998f4E088MWjdunUL2i677ILxdK1Kkzf69u2L5/zoo4+CdvTRRwettFszGWtpnJfMtttuu23QNtxww6CR+V/i8XfBBRcE7aabbsJ4St6h5yejvST17h0/pmi3bLr/UvUE2tmb2o/aTuJ7PfDAA4NWmnvuuuuuoC233HJBmzBhAsbTPEGJBqXdxrfbbrugnXLKKUEjUzm1hyQNHTo0aG3btg3a7bffjvF0LUreKCVK7LbbbqhXwhwXUznniZIm1vz31ymlUZJiXRRjjDHGmP9C6uSZSiktL2lNScNrpMNTSq+nlG5IKXGesjHGGGPMb5iKF1MppUUl3S3p6JzzV5KulrSipM6a9c3VZYW47imlESmlEaWfWowxxhhj5lcqWkyllBbSrIXUgJzzYEnKOU/OOc/IOc+U9HdJ8QfoWcf1yzl3yTl3adSo0c9138YYY4wx8wSVZPMlSddLGpVz/suP9B9vXb6zpOhcM8YYY4z5jVNJNt8GkvaR9K+U0qs12imS9kwpdZaUJX0g6ZA5XmzBBbXkkkvOppV++pu1hpsdcuWTJnFWRu1rS9JSSy2F8ZTBQWUB6PqlreqpLEH9+vWD1qxZM4ynkgx0/VI8PT99W0jHSfxOF1pooYrj6V6pJAiVUymds9I2KWVY0jNRllXpnVKZCyrxQiVuJH4uKsdT6qc0TuidlN4fPReV6aB2aty4MZ6T+im1aemZGjZsGDS6/1KbkE7ZYHSfEj8r3Wtd3im1aV3GDvUpOpbaTqp8PqVM1tKxdP+lPlHpmC6VXqG2qkubUNknOva7774LGs3RUuXzRClzrFWrmMdV6Xwm8fPPnDkzaNT3S9eid1L6PKM2qbTvl9qJnonmg9LYp7aiObpUXqx0X5VQSTbfs5LiSJQe+slXNcYYY4z5jeAd0I0xxhhjqsCLKWOMMcaYKvBiyhhjjDGmClLJiPVL0KBBg9yyZcvZtJK5jkqKDBgwIGhkLpOkffbZJ2jPPPNM0ErlbHr27Bm0gQMHBo0Mm99++y2ek0ztVNKCttSXpJ133jloV199ddBqv+MfoFITVCrg/PPPx/gjjzwyaGRsLd3/p59+GrSmTZsGbYcddgja6NGj8ZxU/mLZZZcNWinRgUrkTJs2LWhUuqF0LPWJQw7h/Iw777wzaNOnTw8avXuJza3UT0t9koy548ePD9pqq60WNCrHIUkvvfRS0LbccsugHXvssRhPZULo/ZdKl5Dhlsqh3H333Ri/6667Bm3QoEFBK5m1qXQMlcMhU3mpTMbnn38eNCoRQ6WMJC5bRGWfSgbyf/7zn0GjPlkaZ2QsplJSVKJG4jInZABv164dxtPc99VXXwWNzM7ffPMNnnPvvfcOGpXDoXlPkk444YSg0WdcyRRN5cmonMvkyZMxvk+fPkFbYYUVgkbzsSTdd999QaP1BH0WX3fddXhOYr/99gsavSeJy8HQOCuZ6vv37x+00aNHj8w5d5nTffqbKWOMMcaYKvBiyhhjjDGmCryYMsYYY4ypAi+mjDHGGGOqoJId0H82FlhggWDwK+1MTeZC2kG8ZA6knb3JLFwy4NP1yZxIhsvSOcksX5d6hWSao3dCBmaJ3zWdk55T4nul69N7Lp2X7pXus2RsbdCgQdBoF+OSWZmeidqv9E5nzJhR0TmpP0n8TuheS/2E3j/dU2mckYGddlGmcVZ6JrpX6me0g3YpnrTS2Cedrl96p3Qsvb8vvvgC46n/0Tmpn5X6Kb0rGhOleLp/2i26NPap/5dMvAQdS/2s1E8rbf/SPEHPRcbkUkITQc9EY4/mqFJ8Xea+Svt0qZ2o/9H1S+OEdBoTdP1SQgwlJNUlvtJxVnonpf5XCf5myhhjjDGmCryYMsYYY4ypAi+mjDHGGGOqwIspY4wxxpgq8GLKGGOMMaYK5mo5mZYtW+Y///nPs2kfffQRHktlNi6//PKgUTkMSTr88MOD9tBDDwXt6aefxviTTz45aIceemjQll566aCVMgU+++yzoF1xxRVBu+SSSzC+V69eQaNyMO3bt8d4KvPRr1+/oFGJFYlLaowZMyZoVHpCkoYMGRI0Kh9BpS9GjhyJ56T779ixY9Aoa02SjjrqqKBRltC1116L8VRShEp6/O1vf8P4I444ImiUJVfqE5Q5eeONNwaN2l6Sdtxxx6C99957QVtjjTWCtv7661d8zqeeeipoxx13HMb/9a9/Ddouu+wStDZt2mD82muvHTQqxbT77rtjPM09Xbt2DdoGG2yA8VTOhspcUEbSDTfcgOekPrXZZpsFjcajJHXr1i1op59+etA++OADjKe2PuaYY4JGGYISj78LL7wwaFTyS+ISWdtss03QSuVk+vbtGzSaJykTs/RMVA6JynuNGzcO4//yl78EjdqJyhtJ0uDBg4NG5bWobI4knXvuuUGjsj1UIkfie6V+fuKJJ1Z0bUl68skng3bzzTcHrVROhuYeKllHmZyS1KNHj6BdeumlLidjjDHGGPNL48WUMcYYY0wVeDFljDHGGFMFc1xMpZQappReTCm9llJ6M6V0do3eNqU0PKU0JqV0e0qJf1g2xhhjjPkNU8ne+d9K2iTn/O+U0kKSnk0pPSzpGEl9cs6DUkrXSDpIUnTf/YicczBdlraFJ9MfGYNLZT4qLdNRiq+0dA3Fl0oS0LFUUqH0TsjESecsxVd6bOmdUJvQ9vtk4pT4/dGxVKagdE+V3n/pnVBb0XOWynTQtSptZ4nfCV2/9E6pzAids1R6hc5baTkeGiMS99NKryPxM9WlTem8FF96J2Tqr8vcQzq9k3r16gWtLmO3Uk3iPkVlSkqlSyo1ZqeUMJ7alEqvlBJF6P3Tu6rL3EPx1M9LY5d0ii/dE+nVPhO907r0UzpnqRxOpfN5peOxdM669HO6V5rjS6WsSnNCJczxm6k8ix+s7wvV/JMlbSLprhq9v6SdfvJdGGOMMcbMp1TkmUop1UspvSrpE0lDJb0naWrO+Ydl+DhJrX6ZWzTGGGOMmXepaDGVc56Rc+4sqbWkdSV1oMMoNqXUPaU0IqU0ovQVsjHGGGPM/EqdsvlyzlMlPSVpPUlNU0o//BjZWtKEQky/nHOXnHMX8sIYY4wxxszPzNGAnlJaStJ3OeepKaWFJW0m6WJJwyTtJmmQpP0k3Tenc82cOTMYHEuGLzKIkTmxFE/mzl8inrSSCZOOJcNgXd4JfdtXiqdjK33PEr8TMvyV4iu9VzIMls5JOl2nLs9E1y+9U9Kr7RPUJiUTKPWfujx/pX2atLoYUykhpHRPRLXPVGlCi1T5OKtLPy/tol3pOatpJ4nff10SFei8NE6oP0qcVFLp2CnpdXn+Sq9FZumSWbnSeaouc0+1z0Rjsi670ldrQK+0n9Slnesyd9CxNPeW2rQuc1JtKsnmayGpf0qpnmZ9k3VHzvmBlNJbkgallM6T9Iqk63/yXRhjjDHGzKfMcTGVc35d0pqgj9Us/5QxxhhjzH8t3gHdGGOMMaYKvJgyxhhjjKkCL6aMMcYYY6qgEgP6z8YCCyygRRdddDZtkUUWwWPJbd+4ceOg1T7fD1AGA12rtF0DlUmh65NWOidlCjRt2jRopWeiUgOLLbZYRfckSU2aNAkalc4oxVOmBJVUKN1/Xe610nNSPD1nKXuj0iyn0vWpfAQdS5k7Et8r9b0vvvgC4yvtP3XpE5VqpTIb1b5TGrt0fepPEj/rv//976DRe5Zmlb2qDd1rXfo5jd1q26nS+Uji0j90LLWdxPdaKptFTJkyJWg0H5fapNL3X3p+mpPpnVI2X6lsEj1/XdqE5l56J6V4en7q56SVrkXnLJWzqfQzktq+NHaonWjsUNtJPCeV+hRRuq9K8DdTxhhjjDFV4MWUMcYYY0wVeDFljDHGGFMFXkwZY4wxxlRBIrPlL0WDBg1yixYtZtNat26Nx3bt2jVoRxxxRNBWWWUVjD/++OODNmTIkKA9++yzGH/WWWcFrW/fvkH7/PPPg1bavn/xxRcP2v777x+0Sy65pOJ7OvPMM4O29NJLYzyVuaB4uo4knXHGGUGj8hF33303xo8ZMyZo9E569eoVtGHDhuE5H3vssaDVxRhLz0SGzeuuuw7jJ06cGLQll1wyaAceeCDGX3nllUEjs/yJJ56I8dOnTw/axRdfHLSSsbJ58+ZB++CDD4LWsWPHoG2zzTZ4zscffzxoO++8c9DOOeccjD/88MODRu//008/xfhll102aAcddFDQbr75Zozv1q1b0Pr37x+0jz76CONbtmwZtEMOOSRoNB4vu+wyPCfNKe3atQva6NGjMf6UU04JWp8+fYK20korYfyoUaOCdswxxwStVI6jWbNmQfvb3/4WtNLnAfVJ6vudOnXCeJp73n333aDR52HJAE5jktpviSWWwPgDDjggaDfeeGPQSgbqr776Kmg0n5WSV84999ygUd89+OCDMX7gwIFBo89D6ielzxjqJzQf0Gdx6VgaZ6VECzrv6NGjR+acu2DAj/A3U8YYY4wxVeDFlDHGGGNMFXgxZYwxxhhTBV5MGWOMMcZUwVzdAb1+/fpabrnlZtPIACtJSy21VNBWXXXVoL3zzjsYTztDv/3220ErmQvJxE3mTDI7kzFSYnMdPSeZACWptnlfYnNjyXDYqlWroJHhs9Qm9K7ILE3GTonf32uvvVbRdciAKrHZe9KkSUErJVpQmzRo0CBoH374IcZT+1HfK73T2uNB4l3VS32CzJW0Azf1HUl66623gka7lZOBt/RMK6ywQtCo75USNej+KT6lhPHUVssss0zQll9+eYynd03tNG7cOIwnYzDd/5dffonxBL0rGhNff/01xtN8RmbtNm3aYPyLL74YNBp7JQM6zVNTp04N2kYbbYTxtAv2G2+8EbT33nsP49u2bRs0uldK1Cj1M+r/tKs5zQcS90nqe/TuJalLl+iJrnac0RxNc5zEiR6vvvpq0KhPUXtK3E70TkrJH3SvNCZKVUpoTiglddTG30wZY4wxxlSBF1PGGGOMMVXgxZQxxhhjTBXMcTGVUmqYUnoxpfRaSunNlNLZNfpNKaX3U0qv1vzT+Ze/XWOMMcaYeYtKDOjfStok5/zvlNJCkp5NKT1c8/+Ozznf9cvdnjHGGGPMvE2dysmklBpJelbSoTX/PFCXxVTLli1z7bIKpSypRx55JGjnnXcenRPjaat/KnPy1FNPYfzZZ58dtP322y9olJFRyhSg8hf9+vWr6NqSdOihhwbt0ksvDVr79u0x/qWXXgraBRdcELRS6RIqx0PZlKXSL/fcc0/QNtlkk6AdeeSRQXvmmWfwnNdee23QKEuplGV00kknBY0y5Kjsi8QZSZRldfXVV2P8YYcdFjTKKDrttNMwnjIfr7rqqqBR20vSrrvuGjTKxuzcOX7x/Pvf/x7PudlmmwXtwQcfDBr1PYlLx2y99dZBK2WeUZbT5ptvHrR99tkH46nMDJUD2nDDDTGeMmSpn1M2FfVniedJOmcpk5bK6Rx99NFBGzt2LMbTtU499dSglTLfqMzI5ZdfHjSa9yXOHNtuu+2CViov1rt376BRiaTvvvsuaKXyVDR30Dj/+OOPMf78888PGpUX22OPPTD+zjvvDBqVoyllzlHpG8quvuOOOzCeSkTtueeeQaO+R3OUJA0dOrSiYx9++OGgSdLuu+8eNMrwo5JhEpeSuuSSS36+cjIppXoppVclfSJpaM55eM3/Oj+l9HpKqU9KKeaTG2OMMcb8xqloMZVznpFz7iyptaR1U0qrSjpZ0iqS1pHUTBJ+nZFS6p5SGpFSGkF/4zfGGGOMmZ+pUzZfznmqpKckbZVznphn8a2kGyWtW07g4FsAABMzSURBVIjpl3PuknPuUvr5yxhjjDFmfqWSbL6lUkpNa/57YUmbSXo7pdSiRkuSdpIUt6M1xhhjjPmNM0cDekppdUn9JdXTrMXXHTnnc1JKT0paSlKS9KqkHjlndnXV0KhRo9yuXbvZNCpJIEk77rhj0MisXSqfQCbqAQMGBK1kgD/hhBOCdttttwXtk08+CVrJ7EyGUzK1k1Fekg444ICgXXLJJUEjA7PEpjsy9ZcM8IcffnjQqJzM9ddfj/FU1oDeVY8ePYL25JNP4jmHDx8eNPoGtPQT88knnxw0KgnRt29fjKfnJ+iZJO5TZILt1asXxtNzXXPNNUFbeOGFMZ7eP5UjojITJWPsyJEjg0YGcDJAl/TBgwcHrTT2GzZsGDRK3qCECImNtbfffnvQSmZtMqCTgZ3KzpSMudQnyZQ9YcIEjD/99NOD9sADDwSNyr5IbHamBILS3EflbHr27Bm0rl27Yvybb74ZNHp/9E4kacaMGUEjYzgldNB4lLhNL7744qCVyoudccYZQaP5oGSWpjF9yimnBG3y5MkYf+GFFwaNknfI6C9x/3n//feDRn3vnHPOwXNS2Sv6jCTzviQde+yxQaN+UvqV7NZbbw3a8OHDKzKgz3FrhJzz65LWBD2mdxhjjDHG/JfhHdCNMcYYY6rAiyljjDHGmCrwYsoYY4wxpgoqKSfzszFjxgx9+eWXs2klA/xnn30WNDq2adOmGE877tJOqLRbden6ZHglc2K9evXwnBRP1ylBO6jPnDkzaGR0l6RJkyYFjQz0ZNiT2JhMz1pKKhg/fnzQyPBK76R0TjK8ktm11CZ0LTL7kjFV4t2F6ZzUdhKbS7///vuK48mATkZ/Gg+StPzyywftm2++CRq1U+meKn3+UlIAxU+ZMiVo1PclropA1y+9EzqW7qlk6m/SpElF56RxVkoeqT1vStwmJbMyjfPSsQT1f3onJQM67YxOJuDSfEhtRXMCzfESG9gJms9pPErcpossskjQSs9UaZ8sVfmg+ZTOWRqnNH7o+qV4OpY+j6nvlfo5fcZQfLNmzTCe7pU+d6mdJJ5nKsXfTBljjDHGVIEXU8YYY4wxVeDFlDHGGGNMFXgxZYwxxhhTBV5MGWOMMcZUwVzP5ps6depsWsnVT9lXlH1SyvKiMh8NGjTAeyIWWCCuMynTgDIiStkf9Ex0nVL5AspmpGNLWU6UaUPHUjaXxO+P4kuZc5S9RJl3VA6kBGVq0PsvtcmCC8YhQFqp/EClGVGUzSTx/VOfKGW90rum91zKsqJ4en7KEKT7LF2L7p/6U0mnzL9SPyu969pQ1qbE90pjotQmNKfQOKH3R9mhErcJxZfmM3pXdclcojahe6pLmyy++OJBK/VTan8a06U+SfGVnrM0H1baz2iOK0HXKr3TSufzEpW+k9L16VjKJK/LZ2RpTNam1M7UJnXpp3XJcA339JMjjTHGGGOMF1PGGGOMMdXgxZQxxhhjTBV4MWWMMcYYUwVz1YDesGFDrbLKKrNpzZs3x2Pbtm0bNDKNUYkUictkPPDAA0ErGeBXXHHFoNW+d0l6//33g0ZmXUlq1apV0Og5S2Uu2rVrFzQy2tM9SdJKK61U0TnpniSpTZs2FV3/wQcfxPgWLVoEjco89OzZM2jPP/88npOeifpEyZhJz0rGzsmTJ2M8lRSh8g90nxL3CTK7t2/fHuPJBErxrVu3xngqv0CJFqNGjQraTjvtVPE56fmpP0nSCiusEDR6/lIpqHfffTdoPXr0CNqqq66K8SuvvHLQOnXqFLRXXnkF419//fWgde/ePWiUKFAq5UTzFM0TpXiaz6j0Sqmffvjhh0GjdioZ6Kn0DZmVS/2cTMQff/xx0CZOnIjxNHePHDkyaGRsLo0delf0/pdddlmMp2elPlnq5xRPfbdUeoXGH91rqU+sttpqQXviiSeCRu+eSsRIUufOnSu6/tixYzGenp/apFQKit7phAkT8Nja+JspY4wxxpgq8GLKGGOMMaYKvJgyxhhjjKmCihdTKaV6KaVXUkoP1Py5bUppeEppTErp9pQSG4WMMcYYY37D1MWAfpSkUZKa1Pz5Ykl9cs6DUkrXSDpI0tX/1wmmT58eTIOlnVDJ8Ft793SJd9GV2ATbuHHjoL344osVX58Mj2SkI1O2xMbmkhGPICMcPSeZ7yVp3LhxFZ3zo48+wngyvNIu0KUdzMlISWZteidNmjQJmsRtQn2i1M/o/dGu5KU2JbM7xZeMsdTP6J2WTJBk+KVdfEs7+9L7JxMuHVd6Juo/dGypn9E7GT9+fNDoPUucVEDvr2TspXulY8n8L/H4o+tTm5R2Zqa5b7nllqs4nvop9f3SrvT0rqmdSjuYT58+PWj0TDSepXJb1YZM7ZI0ZsyYoFW6A3qpnamfUKJLKXmF+gQlD9HnllS5Af+zzz7DeEoqIKN/aZzTvZLZne6zlKRFcwK9p1Llk0rn40UWWaTi61dKRd9MpZRaS9pW0nU1f06SNpF0V80h/SVxao8xxhhjzG+YSn/mu1zSCZJ+WHYvIWlqzvmHZfw4STHH2xhjjDHmN84cF1Mppe0kfZJz/vGmHFThEat+ppS6p5RGpJRG1KUIozHGGGPM/EAlnqkNJO2QUtpGUkPN8kxdLqlpSmnBmm+nWktCU0fOuZ+kfpJUv359LrNujDHGGDOfMsdvpnLOJ+ecW+ecl5fUVdKTOedukoZJ2q3msP0k3feL3aUxxhhjzDxKosyh4sEpbSzpuJzzdimlFSQNktRM0iuS9s45cy2BGho3bpxrbxdPGWaS1KFDh6BRSQXKaJE4A2GjjTYK2tChQzGeslIoU6DSciASZ6nRT5+l8gX0rih7gkrESNLLL78ctFm5BLNTKglB90WZIltssQXG33333UHbdNNNg0ZZIjvvvDOes1+/fkGj8gWUTSTx/VM5lY4dO2I8tSlln5SyCen9U6kDyrAr6dRO1PaStPvuuweNMp/WWmutoN13H//96Z///GfQNt5446DRs0v8rigjisaeJHXp0iVoTz/9dNDoOSUuvUKZT/RMEpfkeOaZZ4JGz1nqZ9Sn/vjHPwbtnXfewXi6f+rnpVJUW265ZdBo7JSyRmnuphIzgwcPxnjK0Nxqq62CVioRdP755wdtjTXWCNo333wTtNLYo2xEKn1CWZOSNGXKlKBRxvRee+2F8bfffnvQaO6n+5S4TagUUunz5I033gja3nvvHTSae+jzXZIefvjhoFHZIio7I/H902csZS1KnHn46quvjsw5x0ml9jnndMCPyTk/Jempmv8eK2ndusQbY4wxxvzW8A7oxhhjjDFV4MWUMcYYY0wVeDFljDHGGFMFdfJMVUvjxo2D4ZiMkRIbES+77LKglbaV32+//YJ23HHHBa1kDD711FODdvjhhweNSpeUDOhk+LziiiuCtv/++2M8mSjJmL3yyitjPN3XvffeG7QddtgB46+66qqg0fs788wzMZ4Ml7/73e+CRu+kd+/eeM633347aGTiLZWEOPTQQ4NGJTlKbUKJCu+9917QbrnlFow/6KCDgkblI6677jqMJ3Mr9f1SmY111422RzIGr7baakE7++yz8Zy77LJL0EaMGBG0fffdF+MvuuiioNEzlQzoa665ZtD69+8ftO222w7jH3vssaBts802QVtnnXUwnu5r0KBBQaP54LDDDsNzUp+isbP00ktj/MCBA4N2/PHHB62UEETtT/NpqUwHlQ/p1atX0Ep7EVKfJAM+JQ9IPKeRqZ3ms1KJHZrnaJ4ofcbdeuutQdt2222DRu0s8dinRIeSAZ3ef5s2bYJGfUeSdt1116Ctv/76QXvhhReCVkreoLn3gQceCBrN2xInJH333Xd4LNGjR4+gvfrqqxXF+pspY4wxxpgq8GLKGGOMMaYKvJgyxhhjjKkCL6aMMcYYY6qgTjugV0v9+vXzUkstNZtGu7BKbGIdMmRI0ErxtDsu7Q783HPPYXy3bt2CdscddwSNzG2lnZ1pd9099tgjaMOHD8f4TTbZJGi33XZb0MhYKUmvvfZa0MiEWjJ7kzmPoJ1xJWnUqFFBo3slE+OXX36J57z55puDRjv2klFcknbcccegff3110H7xz/+gfFfffVV0GgX3UMOOQTjKQGATLhkVJe4Tz366KNBox3EJWn11VcPGhlmabfoUj+j/kvjkRIqJDab0+75ZGqWuP3JLH7PPfdgPPWJO++8s+Lr047Na6+9dtBot/Bhw4bhOcePHx80Gidjx47FeDJgP/HEE0Gj3fcl6d133w1a165dg1YaZ5QoVGn1Akl66623gkbvuZR8Q7vy0zijNi0ZmLfffvugkVmadq+XeE4YMGBA0Dp16oTxb775ZtBOPPHEoFGVBkm66aabgkZGfzLFS9L9999fUTyNfZr3JN4BnhJVKElE4ion06ZNC1qpn9P7/+ijjyraAd3fTBljjDHGVIEXU8YYY4wxVeDFlDHGGGNMFXgxZYwxxhhTBV5MGWOMMcZUwVwtJ5NzDtv1l7KMyNXfsGHDoJXKJ1CWVYsWLYJG5WAkLvVAWV6UvUD3KfEz0XVKWUL0rpo0aRK0Zs2aYTwdS+csZTpQVsS3334btGWWWQbjKZuQsnyo7UrtTM9EbVoqc0GlFupyfcrcpHgq/SBJM2bMCNpCCy0UtFJGDpW/oNIxpSwvaitq5+bNmwfts88+w3NS9tKkSZOCVpexS/dUGrt0Xnr/pSyrSo9da621MJ7ui8YZZb6Vxm6l44zencRtRRmyNB+VrkXloUplmyhrnMrulMrZ0Dilubc099D4WXbZZSu6zxL0TkvlzQjqZ6SVnunZZ58NGo0z+tyReJ6g+FKfoDHRvn37is5Z6ueU8U1tV3tXgB+gZ6WMZ9Ik7tOV4m+mjDHGGGOqwIspY4wxxpgq8GLKGGOMMaYKvJgyxhhjjKmCuVpOJqX0qaQfalUsKYkdrGZewu00f+B2mj9wO80/uK3mD37pdlou58yO9x8xVxdTs104pRGV1Lsxvy5up/kDt9P8gdtp/sFtNX8wr7STf+YzxhhjjKkCL6aMMcYYY6rg11xM9fsVr20qx+00f+B2mj9wO80/uK3mD+aJdvrVPFPGGGOMMb8F/DOfMcYYY0wVzPXFVEppq5TS6JTSuymlk+b29Q2TUmqTUhqWUhqVUnozpXRUjd4spTQ0pTSm5t9cEM3MVVJK9VJKr6SUHqj5c9uU0vCadro9pcQFHs1cJaXUNKV0V0rp7Zqx9TuPqXmPlFKvmnnvjZTSwJRSQ4+peYOU0g0ppU9SSm/8SMMxlGZxRc364vWUEhfQ/AWYq4uplFI9SVdJ2lpSR0l7ppQ6zs17MEW+l3RszrmDpPUk9axpm5MkPZFzXlnSEzV/Nr8+R0n6cVXQiyX1qWmnKZIO+lXuytSmr6RHcs6rSFpDs9rMY2oeIqXUStKRkrrknFeVVE9SV3lMzSvcJGmrWlppDG0taeWaf7pLunou3eNc/2ZqXUnv5pzH5pynSxokace5fA8GyDlPzDm/XPPfX2vWpN9Ks9qnf81h/SXt9OvcofmBlFJrSdtKuq7mz0nSJpLuqjnE7TQPkFJqImlDSddLUs55es55qjym5kUWlLRwSmlBSY0kTZTH1DxBzvlpSV/UkktjaEdJN+dZvCCpaUqpxdy4z7m9mGol6eMf/XlcjWbmIVJKy0taU9JwSc1zzhOlWQsuSUv/endmarhc0gmSZtb8eQlJU3PO39f82eNq3mAFSZ9KurHmJ9nrUkqLyGNqniLnPF5Sb0kfadYi6ktJI+UxNS9TGkO/2hpjbi+mEmhOJ5yHSCktKuluSUfnnL/6te/HzE5KaTtJn+ScR/5YhkM9rn59FpS0lqSrc85rSpom/6Q3z1Hjt9lRUltJLSUtolk/F9XGY2re51ebC+f2YmqcpDY/+nNrSRPm8j2YAimlhTRrITUg5zy4Rp78w9ekNf/+5Ne6PyNJ2kDSDimlDzTrZ/JNNOubqqY1P1FIHlfzCuMkjcs5D6/5812atbjymJq32EzS+znnT3PO30kaLGl9eUzNy5TG0K+2xpjbi6mXJK1ckyVRX7NMfkPm8j0YoMZ3c72kUTnnv/zofw2RtF/Nf+8n6b65fW/m/5NzPjnn3DrnvLxmjZ8nc87dJA2TtFvNYW6neYCc8yRJH6eU2tdIm0p6Sx5T8xofSVovpdSoZh78oZ08puZdSmNoiKR9a7L61pP05Q8/B/7SzPVNO1NK22jW36TrSboh53z+XL0Bg6SUfi/pGUn/0v/34pyiWb6pOyQtq1mTzp9yzrXNgOZXIKW0saTjcs7bpZRW0KxvqppJekXS3jnnb3/N+zNSSqmzZiUK1Jc0VtIBmvWXWI+peYiU0tmS9tCsrOZXJB2sWV4bj6lfmZTSQEkbS1pS0mRJZ0q6VzCGahbDV2pW9t//Sjog5zxirtynd0A3xhhjjPnpeAd0Y4wxxpgq8GLKGGOMMaYKvJgyxhhjjKkCL6aMMcYYY6rAiyljjDHGmCrwYsoYY4wxpgq8mDLGGGOMqQIvpowxxhhjquD/ATVNkCrvEPaDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 44 / 45 correct => accuracy: 0.977778\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "y_test_pred = y_test_pred.reshape((-1, 1))\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / X_test.shape[0]\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45, 1) (45, 1)\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape, y_test_pred.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 44 / 45 correct => accuracy: 0.977778\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "y_test_pred = y_test_pred.reshape((-1, 1))\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / X_test.shape[0]\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 28.127807 seconds\n",
      "One loop version took 112.870033 seconds\n",
      "No loop version took 0.805844 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.904762\n",
      "k = 1, accuracy = 1.000000\n",
      "k = 1, accuracy = 0.952381\n",
      "k = 1, accuracy = 0.857143\n",
      "k = 1, accuracy = 0.952381\n",
      "k = 3, accuracy = 0.857143\n",
      "k = 3, accuracy = 1.000000\n",
      "k = 3, accuracy = 0.952381\n",
      "k = 3, accuracy = 0.857143\n",
      "k = 3, accuracy = 0.952381\n",
      "k = 5, accuracy = 0.857143\n",
      "k = 5, accuracy = 1.000000\n",
      "k = 5, accuracy = 0.952381\n",
      "k = 5, accuracy = 0.904762\n",
      "k = 5, accuracy = 0.952381\n",
      "k = 8, accuracy = 0.904762\n",
      "k = 8, accuracy = 1.000000\n",
      "k = 8, accuracy = 0.952381\n",
      "k = 8, accuracy = 0.904762\n",
      "k = 8, accuracy = 0.952381\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 10, accuracy = 1.000000\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 10, accuracy = 0.904762\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 12, accuracy = 1.000000\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 12, accuracy = 0.857143\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 15, accuracy = 1.000000\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 15, accuracy = 0.857143\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 20, accuracy = 1.000000\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 20, accuracy = 0.761905\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 50, accuracy = 1.000000\n",
      "k = 50, accuracy = 1.000000\n",
      "k = 50, accuracy = 0.904762\n",
      "k = 50, accuracy = 0.761905\n",
      "k = 50, accuracy = 0.904762\n",
      "k = 100, accuracy = 0.285714\n",
      "k = 100, accuracy = 0.380952\n",
      "k = 100, accuracy = 0.333333\n",
      "k = 100, accuracy = 0.238095\n",
      "k = 100, accuracy = 0.190476\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "\n",
    "for k in k_choices:\n",
    "    for fold in range(num_folds): #This fold will be omitted.\n",
    "        #Creating validation data and temp training data\n",
    "        validation_X_test = X_train_folds[fold]\n",
    "        validation_y_test = y_train_folds[fold]\n",
    "        temp_X_train = np.concatenate(X_train_folds[:fold] + X_train_folds[fold + 1:])\n",
    "        temp_y_train = np.concatenate(y_train_folds[:fold] + y_train_folds[fold + 1:])\n",
    "\n",
    "        #Initializing a class\n",
    "        test_classifier = KNearestNeighbor()\n",
    "        test_classifier.train( temp_X_train, temp_y_train )\n",
    "        \n",
    "        #Computing the distance\n",
    "        temp_dists = test_classifier.compute_distances_two_loops(validation_X_test)\n",
    "        temp_y_test_pred = test_classifier.predict_labels(temp_dists, k=k)\n",
    "        temp_y_test_pred = temp_y_test_pred.reshape((-1, 1))\n",
    "        #Checking accuracies\n",
    "        num_correct = np.sum(temp_y_test_pred == validation_y_test)\n",
    "        num_test = validation_X_test.shape[0]\n",
    "        accuracy = float(num_correct) / num_test\n",
    "        k_to_accuracies[k] = k_to_accuracies.get(k,[]) + [accuracy]\n",
    "    \n",
    "\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "best_k = k_choices[np.argmax(accuracies_mean)]\n",
    "print(best_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.904762\n",
      "k = 1, accuracy = 1.000000\n",
      "k = 1, accuracy = 0.952381\n",
      "k = 1, accuracy = 0.857143\n",
      "k = 1, accuracy = 0.952381\n",
      "k = 3, accuracy = 0.857143\n",
      "k = 3, accuracy = 1.000000\n",
      "k = 3, accuracy = 0.952381\n",
      "k = 3, accuracy = 0.857143\n",
      "k = 3, accuracy = 0.952381\n",
      "k = 5, accuracy = 0.857143\n",
      "k = 5, accuracy = 1.000000\n",
      "k = 5, accuracy = 0.952381\n",
      "k = 5, accuracy = 0.904762\n",
      "k = 5, accuracy = 0.952381\n",
      "k = 8, accuracy = 0.904762\n",
      "k = 8, accuracy = 1.000000\n",
      "k = 8, accuracy = 0.952381\n",
      "k = 8, accuracy = 0.904762\n",
      "k = 8, accuracy = 0.952381\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 10, accuracy = 1.000000\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 10, accuracy = 0.904762\n",
      "k = 10, accuracy = 0.952381\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 12, accuracy = 1.000000\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 12, accuracy = 0.857143\n",
      "k = 12, accuracy = 0.952381\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 15, accuracy = 1.000000\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 15, accuracy = 0.857143\n",
      "k = 15, accuracy = 0.952381\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 20, accuracy = 1.000000\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 20, accuracy = 0.761905\n",
      "k = 20, accuracy = 0.952381\n",
      "k = 50, accuracy = 1.000000\n",
      "k = 50, accuracy = 1.000000\n",
      "k = 50, accuracy = 0.904762\n",
      "k = 50, accuracy = 0.761905\n",
      "k = 50, accuracy = 0.904762\n",
      "k = 100, accuracy = 0.285714\n",
      "k = 100, accuracy = 0.380952\n",
      "k = 100, accuracy = 0.333333\n",
      "k = 100, accuracy = 0.238095\n",
      "k = 100, accuracy = 0.190476\n",
      "最佳k值为10\n",
      "Got 43 / 45 correct => accuracy: 0.955556\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from collections import Counter\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.utils import shuffle\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) \n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "\n",
    "class KNearestNeighbor(object):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "\n",
    "    def train(self, X, y):\n",
    "        \"\"\"\n",
    "        Train the classifier. For k-nearest neighbors this is just \n",
    "        memorizing the training data.\n",
    "\n",
    "        Inputs:\n",
    "        - X: A numpy array of shape (num_train, D) containing the training data\n",
    "          consisting of num_train samples each of dimension D.\n",
    "        - y: A numpy array of shape (N,) containing the training labels, where\n",
    "             y[i] is the label for X[i].\n",
    "        \"\"\"\n",
    "        self.X_train = X\n",
    "        self.y_train = y\n",
    "\n",
    "    \n",
    "    def compute_distances(self, X):\n",
    "        \"\"\"\n",
    "        Compute the distance between each test point in X and each training point\n",
    "        in self.X_train using no explicit loops.\n",
    "\n",
    "        Input / Output: Same as compute_distances_two_loops\n",
    "        \"\"\"\n",
    "        num_test = X.shape[0]\n",
    "        num_train = self.X_train.shape[0]\n",
    "        dists = np.zeros((num_test, num_train)) \n",
    "\n",
    "        M = np.dot(X, self.X_train.T)\n",
    "        te = np.square(X).sum(axis=1)\n",
    "        tr = np.square(self.X_train).sum(axis=1)\n",
    "        dists = np.sqrt(-2 * M + tr + np.matrix(te).T)\n",
    "        return dists\n",
    "\n",
    "    def predict_labels(self, dists, k=1):\n",
    "        \"\"\"\n",
    "        Given a matrix of distances between test points and training points,\n",
    "        predict a label for each test point.\n",
    "\n",
    "        Inputs:\n",
    "        - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n",
    "          gives the distance betwen the ith test point and the jth training point.\n",
    "\n",
    "        Returns:\n",
    "        - y: A numpy array of shape (num_test,) containing predicted labels for the\n",
    "          test data, where y[i] is the predicted label for the test point X[i].  \n",
    "        \"\"\"\n",
    "        num_test = dists.shape[0]\n",
    "        y_pred = np.zeros(num_test)\n",
    "        for i in range(num_test):\n",
    "\n",
    "            closest_y = []\n",
    "            labels = self.y_train[np.argsort(dists[i, :])].flatten()\n",
    "            closest_y = labels[0:k]\n",
    "\n",
    "            c = Counter(closest_y)\n",
    "            y_pred[i] = c.most_common(1)[0][0]\n",
    "\n",
    "        return y_pred\n",
    "    \n",
    "    def cross_validation(self, X_train, y_train):\n",
    "        num_folds = 5\n",
    "        k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "        X_train_folds = []\n",
    "        y_train_folds = []\n",
    "\n",
    "        X_train_folds = np.array_split(X_train, num_folds)\n",
    "        y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "        k_to_accuracies = {}\n",
    "\n",
    "        for k in k_choices:\n",
    "            for fold in range(num_folds): #This fold will be omitted.\n",
    "                #Creating validation data and temp training data\n",
    "                validation_X_test = X_train_folds[fold]\n",
    "                validation_y_test = y_train_folds[fold]\n",
    "                temp_X_train = np.concatenate(X_train_folds[:fold] + X_train_folds[fold + 1:])\n",
    "                temp_y_train = np.concatenate(y_train_folds[:fold] + y_train_folds[fold + 1:])\n",
    "\n",
    "                #Initializing a class\n",
    "                self.train(temp_X_train, temp_y_train )\n",
    "\n",
    "                #Computing the distance\n",
    "                temp_dists = self.compute_distances(validation_X_test)\n",
    "                temp_y_test_pred = self.predict_labels(temp_dists, k=k)\n",
    "                temp_y_test_pred = temp_y_test_pred.reshape((-1, 1))\n",
    "                #Checking accuracies\n",
    "                num_correct = np.sum(temp_y_test_pred == validation_y_test)\n",
    "                num_test = validation_X_test.shape[0]\n",
    "                accuracy = float(num_correct) / num_test\n",
    "                k_to_accuracies[k] = k_to_accuracies.get(k,[]) + [accuracy]\n",
    "\n",
    "        # Print out the computed accuracies\n",
    "        for k in sorted(k_to_accuracies):\n",
    "            for accuracy in k_to_accuracies[k]:\n",
    "                print('k = %d, accuracy = %f' % (k, accuracy))\n",
    "                \n",
    "        accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "        best_k = k_choices[np.argmax(accuracies_mean)]\n",
    "        print('最佳k值为{}'.format(best_k))\n",
    "        return best_k\n",
    "        \n",
    "        \n",
    "    def create_train_test(self):\n",
    "        X, y = shuffle(iris.data, iris.target, random_state=13)\n",
    "        X = X.astype(np.float32)\n",
    "        y = y.reshape((-1,1))\n",
    "        offset = int(X.shape[0] * 0.7)\n",
    "        X_train, y_train = X[:offset], y[:offset]\n",
    "        X_test, y_test = X[offset:], y[offset:]\n",
    "        y_train = y_train.reshape((-1,1))\n",
    "        y_test = y_test.reshape((-1,1))\n",
    "        return X_train, y_train, X_test, y_test\n",
    "\n",
    "        \n",
    "if __name__ == '__main__':\n",
    "    knn_classifier = KNearestNeighbor()\n",
    "    X_train, y_train, X_test, y_test = knn_classifier.create_train_test()\n",
    "    best_k = knn_classifier.cross_validation(X_train, y_train)\n",
    "    dists = knn_classifier.compute_distances(X_test)\n",
    "    y_test_pred = knn_classifier.predict_labels(dists, k=best_k)\n",
    "    y_test_pred = y_test_pred.reshape((-1, 1))\n",
    "    num_correct = np.sum(y_test_pred == y_test)\n",
    "    accuracy = float(num_correct) / X_test.shape[0]\n",
    "    print('Got %d / %d correct => accuracy: %f' % (num_correct, X_test.shape[0], accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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